ACTIVITY AND GESTURE RECOGNITION

Within the context of Home Automation, the design of man-machine interfaces have assumed a central role for the development of smart environments. In this respect, the interaction based on gestures measured through inertial devices represents a fascinating and interesting solution thanks to a new generation of ubiquitous technologies that allow to pervasively and seamlessly control the human space.

This research line regards a Machine Learning (ML) approach to gesture recognition (GR), in its main aspects of (i) event identification, (ii) feature extraction and (iii) classification: in detail, an informative and compact representation of the gesture input signals is defined, using both feature extraction and the analysis in the time domain through signal warping, a pre-processing phase based on Principal Component Analysis is proposed to increase the performance in real-world scenario conditions, and, finally, parsimonious classification techniques based on Sparse Bayesian Learning are designed and compared with more classical ML algorithms. These contributions yield the definition of a system that is user independent, device independent, device orientation independent, and provides a high classification accuracy.